{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "t-SNE_test.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/henrygas/unsupervised_learning/blob/master/t_SNE_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HCCioZhfXzuU",
        "colab_type": "text"
      },
      "source": [
        "## 1. 装载google云盘"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6dKQauFXxEF",
        "colab_type": "code",
        "outputId": "15cc1f82-f44f-4494-e77d-cdf71906bffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQq_HBo_YpYe",
        "colab_type": "text"
      },
      "source": [
        "## 2. 定位当前工作目录"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQRd-pzEYtQq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.chdir(\"./drive/My Drive/app/t-SNE_test\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMa5lkxxZNxZ",
        "colab_type": "code",
        "outputId": "50c901bc-8e38-4b9a-91eb-9bcaaacbeaff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data  model  out\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7gzuRKSZQPv",
        "colab_type": "text"
      },
      "source": [
        "## 3. 读取数据，并获取前10000行数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqvfKMSrZVzt",
        "colab_type": "code",
        "outputId": "984cb186-5b4a-46b9-a206-283749c64e9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "data_path = \"./data/Otto_train.csv\"\n",
        "data = pd.read_csv(data_path)\n",
        "data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Class_1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 95 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  feat_1  feat_2  feat_3  ...  feat_91  feat_92  feat_93   target\n",
              "0   1       1       0       0  ...        0        0        0  Class_1\n",
              "1   2       0       0       0  ...        0        0        0  Class_1\n",
              "2   3       0       0       0  ...        0        0        0  Class_1\n",
              "3   4       1       0       0  ...        0        0        0  Class_1\n",
              "4   5       0       0       0  ...        0        0        0  Class_1\n",
              "\n",
              "[5 rows x 95 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JAE1uBYZsHX",
        "colab_type": "code",
        "outputId": "d9167fb0-16ff-4d4c-ada5-62f10b05baca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61878, 95)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3cNSav2Zuzu",
        "colab_type": "code",
        "outputId": "fadfdd4c-234e-4391-941c-823304eed0ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "data_use, data_del = train_test_split(data, train_size=10000, random_state=0)\n",
        "data_use.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>feat_1</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Class_8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 95 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  feat_1  feat_2  feat_3  ...  feat_91  feat_92  feat_93   target\n",
              "7897    7898       0       0       0  ...        0        0        0  Class_2\n",
              "11287  11288       0       0       0  ...        0        0        0  Class_2\n",
              "10355  10356       0       0       0  ...        0        0        0  Class_2\n",
              "13438  13439       1       0       0  ...        0        0        0  Class_2\n",
              "54129  54130       0       0       0  ...        0        0        0  Class_8\n",
              "\n",
              "[5 rows x 95 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwzu3pdYaRpC",
        "colab_type": "code",
        "outputId": "f3fcad35-d18e-413a-dabe-0a6822b719e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "data_use = data_use.reset_index(drop=True)\n",
        "data_use.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Class_8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 95 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  feat_1  feat_2  feat_3  ...  feat_91  feat_92  feat_93   target\n",
              "0   7898       0       0       0  ...        0        0        0  Class_2\n",
              "1  11288       0       0       0  ...        0        0        0  Class_2\n",
              "2  10356       0       0       0  ...        0        0        0  Class_2\n",
              "3  13439       1       0       0  ...        0        0        0  Class_2\n",
              "4  54130       0       0       0  ...        0        0        0  Class_8\n",
              "\n",
              "[5 rows x 95 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLYmcoSkZ3YC",
        "colab_type": "code",
        "outputId": "35c70239-02e3-4cc8-c13c-827d7c534e23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data_use.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 95)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZV_zmY5Z6C8",
        "colab_type": "text"
      },
      "source": [
        "## 4. 对前10000行数据进行t-SNE降维处理，得到降维后的结果"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Efg8aUcjaOAk",
        "colab_type": "code",
        "outputId": "3b333ebd-f382-4eef-e1ae-33e58ab9f55b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "id = data_use[\"id\"]\n",
        "target = data_use[\"target\"]\n",
        "X_train = data_use.drop([\"id\", \"target\"], axis=1)\n",
        "X_train.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        text-align: right;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>feat_1</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 93 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   feat_1  feat_2  feat_3  feat_4  ...  feat_90  feat_91  feat_92  feat_93\n",
              "0       0       0       0       0  ...        0        0        0        0\n",
              "1       0       0       0       0  ...        0        0        0        0\n",
              "2       0       0       0       0  ...        0        0        0        0\n",
              "3       1       0       0       0  ...        0        0        0        0\n",
              "4       0       0       0       0  ...        1        0        0        0\n",
              "\n",
              "[5 rows x 93 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSsS0cycasGM",
        "colab_type": "code",
        "outputId": "e55b140e-a59d-43a1-b6de-a0f0ce871b24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "t_SNE = TSNE(n_components=2, perplexity=300, early_exaggeration=100, learning_rate=200.0)\n",
        "X_train_TSNE = t_SNE.fit_transform(X_train)\n",
        "t_SNE.kl_divergence_"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9579530954360962"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeOH2LnGdUGR",
        "colab_type": "code",
        "outputId": "16dd7dd8-861d-4e50-fbb1-2bc52979c1d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_train_TSNE.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOyQTjhLdZi2",
        "colab_type": "code",
        "outputId": "2ad7a49a-5f9a-4986-9135-a704755da2b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "X_train_TSNE[:10]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ -5.3409   ,   4.387675 ],\n",
              "       [-12.326709 ,  -3.6387887],\n",
              "       [  7.4885755,  -2.5719352],\n",
              "       [-12.096634 ,  -0.9910166],\n",
              "       [  7.00396  ,  20.0006   ],\n",
              "       [ -6.809832 , -10.547211 ],\n",
              "       [-16.75382  , -24.102047 ],\n",
              "       [ -8.448259 , -35.119987 ],\n",
              "       [-13.444921 ,   7.694057 ],\n",
              "       [-14.094679 , -25.895311 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ImAsqs7dmA7",
        "colab_type": "text"
      },
      "source": [
        "## 5. 画出t-SNE变换的后的结果"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UW1T2LVkdjIc",
        "colab_type": "code",
        "outputId": "9ace541e-ead2-44ec-ec3f-f65c10ff0110",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "TSNE_feature_names = list()\n",
        "for i in range(X_train_TSNE.shape[1]):\n",
        "  TSNE_feature_names.append(\"feature_{}\".format(i))\n",
        "\n",
        "X_train_TSNE_df = pd.DataFrame(data=X_train_TSNE, columns=TSNE_feature_names)\n",
        "labeled_df = pd.concat([X_train_TSNE_df, target], axis=1)\n",
        "labeled_df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature_0</th>\n",
              "      <th>feature_1</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-5.340900</td>\n",
              "      <td>4.387675</td>\n",
              "      <td>Class_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-12.326709</td>\n",
              "      <td>-3.638789</td>\n",
              "      <td>Class_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.488575</td>\n",
              "      <td>-2.571935</td>\n",
              "      <td>Class_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-12.096634</td>\n",
              "      <td>-0.991017</td>\n",
              "      <td>Class_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.003960</td>\n",
              "      <td>20.000601</td>\n",
              "      <td>Class_8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   feature_0  feature_1   target\n",
              "0  -5.340900   4.387675  Class_2\n",
              "1 -12.326709  -3.638789  Class_2\n",
              "2   7.488575  -2.571935  Class_2\n",
              "3 -12.096634  -0.991017  Class_2\n",
              "4   7.003960  20.000601  Class_8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdCi5kDBeXLT",
        "colab_type": "code",
        "outputId": "703cd0e8-e2d3-403f-a90b-af7949ef91fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.scatterplot(x=\"feature_0\", y=\"feature_1\", hue=\"target\", data=labeled_df)\n",
        "plt.xlabel(\"feature_0\")\n",
        "plt.ylabel(\"feature_1\")\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6kXQaFbrmz4NQKBS/kPLXq/jZ3A0e9q1qmjfgqPVSU6osAKRQXEqUAKH42QQEBOHshaVV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\nr+2+SV61hTl9Y7mrbyxTe7SlqsGO1Sq7rq7tKuDVSZ1487tMnh7bOoW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94mi04NPiWD8HuCGMO0aLwqAj7ltuiMarQ6NVFqEY/dT1jUr+hv+28iJMktweECFDbWP49aWrWK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KCusKaalryd4Kl/5icWMxj2x9hMV9F2Nz2ghUBbK1aCsHqw7SJaILRrsRu9POoapDjGs7jvcP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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsG7D320AYbW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "852610c2-ffd9-4b40-bbea-baea04592f9e"
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import time\n",
        "from functools import wraps\n",
        "import threading\n",
        "from concurrent.futures import ThreadPoolExecutor\n",
        "\n",
        "def log_search_time_cost(name=None):\n",
        "  def decorator(func):\n",
        "    @wraps(func)\n",
        "    def _do_log(*args, **xargs):\n",
        "      print(\"starting searching {}\".format(xargs[name]))\n",
        "      start_time = time.time()\n",
        "      func(*args, **xargs)\n",
        "      time_cost = time.time() - start_time\n",
        "      print(\"ending searching {}, time_cost:{:.4f}秒\\n\".format(xargs[name], time_cost))\n",
        "\n",
        "    return _do_log\n",
        "\n",
        "  return decorator\n",
        "\n",
        "\n",
        "class SearchParam:\n",
        "  def __init__(self, perplexity, early_exaggeration, learning_rate, n_iter):\n",
        "    self.perplexity_ = perplexity\n",
        "    self.early_exaggeration_ = early_exaggeration\n",
        "    self.learning_rate_ = learning_rate\n",
        "    self.n_iter_ = n_iter\n",
        "\n",
        "  def name(self):\n",
        "    return \"[perplexity={} early_exaggeration={} learning_rate={} n_iter={}]\".format(\n",
        "        self.perplexity_, self.early_exaggeration_, self.learning_rate_, self.n_iter_\n",
        "    )\n",
        "\n",
        "class TSNESearchUnit:\n",
        "  def __init__(self, X, y, search_param):\n",
        "    self.X_ = X\n",
        "    self.y_ = y\n",
        "    self.search_param_ = search_param\n",
        "    self.search_name_ = self.search_param_.name()\n",
        "\n",
        "\n",
        "  @log_search_time_cost(name=\"search_name\")\n",
        "  def search(self, search_name=None):\n",
        "    self._single_search()\n",
        "\n",
        "  def _single_search(self):\n",
        "    \"\"\"\n",
        "    一次搜索\n",
        "    :param perplexity: 混乱度, 表示优化过程中考虑邻近点的多少\n",
        "    :param early_exaggeration: 嵌入空间簇间距的大小，默认为12\n",
        "    :param learning_rate: 学习率，表示梯度下降的快慢，默认200\n",
        "    :param n_iter: 迭代次数\n",
        "    \"\"\"\n",
        "\n",
        "    perplexity, early_exaggeration, learning_rate, n_iter = self.search_param_.perplexity_, \\\n",
        "                                   self.search_param_.early_exaggeration_, \\\n",
        "                                   self.search_param_.learning_rate_, \\\n",
        "                                   self.search_param_.n_iter_\n",
        "\n",
        "    each_tsne = TSNE(n_components=2, \n",
        "                     perplexity=perplexity, \n",
        "                     early_exaggeration=early_exaggeration, \n",
        "                     learning_rate=learning_rate, \n",
        "                     n_iter=n_iter)\n",
        "    each_X = each_tsne.fit_transform(self.X_)\n",
        "\n",
        "    feature_names = list()\n",
        "    for i in range(each_X.shape[1]):\n",
        "      feature_names.append(\"feature_{}\".format(i))\n",
        "    \n",
        "    each_X_df = pd.DataFrame(data=each_X, columns=feature_names)\n",
        "    each_df = pd.concat([each_X_df, self.y_], axis=1)\n",
        "\n",
        "    title = \"perplexity={}, early_exaggeration={}, learning_rate={}, n_iter={}\".format(\n",
        "        perplexity, early_exaggeration, learning_rate, n_iter\n",
        "    )\n",
        "\n",
        "    pic_name = \"p-{}_e-{}_l-{}_n-{}\".format(perplexity, early_exaggeration, learning_rate, n_iter)\n",
        "\n",
        "    self._draw(each_df, title, pic_name)\n",
        "    print(\"完成一个！！！\")\n",
        "\n",
        "  def _draw(self, each_df, title, pic_name):\n",
        "    plt.subplots()\n",
        "    sns.scatterplot(x=\"feature_0\", y=\"feature_1\", hue=\"target\", data=each_df)\n",
        "    plt.title(title)\n",
        "    plt.xlabel(\"feature_0\")\n",
        "    plt.ylabel(\"feature_1\")\n",
        "    plt.savefig(\"./out/{}.png\".format(pic_name))\n",
        "\n",
        "\n",
        "class SearchThread(threading.Thread):\n",
        "  def __init__(self, search):\n",
        "    super(SearchThread, self).__init__()\n",
        "    self.search_ = search\n",
        "\n",
        "  def run(self):\n",
        "    self.search_.search(search_name=self.search_.search_name_)\n",
        "\n",
        "\n",
        "def search_once(search_unit):\n",
        "  search_unit.search(search_name=search_unit.search_name_)\n",
        "\n",
        "perplexities = [30, 100, 500]\n",
        "early_exaggerations = [12, 100, 500]\n",
        "learning_rates = [200, 10, 500]\n",
        "n_iteres = [1000, 2000, 3000]\n",
        "\n",
        "tasks = list()\n",
        "for perplexity in perplexities:\n",
        "  for early_exaggeration in early_exaggerations:\n",
        "    for learning_rate in learning_rates:\n",
        "      for n_iter in n_iteres:\n",
        "        search_param = SearchParam(perplexity, early_exaggeration, learning_rate, n_iter)\n",
        "        search_unit = TSNESearchUnit(X_train, target, search_param)\n",
        "        search_once(search_unit)\n",
        "\n",
        "for task in tasks:\n",
        "  task.result()\n",
        "\n",
        "print(\"All Over!\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=200 n_iter=1000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=200 n_iter=1000], time_cost:125.9501秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=200 n_iter=2000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=200 n_iter=2000], time_cost:219.0634秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=200 n_iter=3000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=200 n_iter=3000], time_cost:317.3880秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=10 n_iter=1000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=10 n_iter=1000], time_cost:120.7679秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=10 n_iter=2000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=10 n_iter=2000], time_cost:221.2267秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=10 n_iter=3000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=10 n_iter=3000], time_cost:321.7493秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=500 n_iter=1000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=500 n_iter=1000], time_cost:125.3357秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=500 n_iter=2000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=500 n_iter=2000], time_cost:236.0120秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=12 learning_rate=500 n_iter=3000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=12 learning_rate=500 n_iter=3000], time_cost:323.0355秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=200 n_iter=1000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=200 n_iter=1000], time_cost:145.3484秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=200 n_iter=2000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=200 n_iter=2000], time_cost:249.2651秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=200 n_iter=3000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=200 n_iter=3000], time_cost:330.7493秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=10 n_iter=1000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=10 n_iter=1000], time_cost:120.5366秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=10 n_iter=2000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=10 n_iter=2000], time_cost:226.5838秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=10 n_iter=3000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=10 n_iter=3000], time_cost:314.1416秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=500 n_iter=1000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=500 n_iter=1000], time_cost:140.7521秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=500 n_iter=2000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=500 n_iter=2000], time_cost:226.9818秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=100 learning_rate=500 n_iter=3000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=100 learning_rate=500 n_iter=3000], time_cost:316.8916秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=200 n_iter=1000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=200 n_iter=1000], time_cost:137.0221秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=200 n_iter=2000]\n",
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=200 n_iter=2000], time_cost:237.3945秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=200 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=200 n_iter=3000], time_cost:331.0356秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=10 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=10 n_iter=1000], time_cost:148.3912秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=10 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=10 n_iter=2000], time_cost:268.2290秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=10 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=10 n_iter=3000], time_cost:400.8282秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=500 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=500 n_iter=1000], time_cost:136.1449秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=500 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=500 n_iter=2000], time_cost:235.7211秒\n",
            "\n",
            "starting searching [perplexity=30 early_exaggeration=500 learning_rate=500 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=30 early_exaggeration=500 learning_rate=500 n_iter=3000], time_cost:341.6793秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=200 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=200 n_iter=1000], time_cost:163.6333秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=200 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=200 n_iter=2000], time_cost:294.9959秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=200 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=200 n_iter=3000], time_cost:430.9713秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=10 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=10 n_iter=1000], time_cost:159.5334秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=10 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=10 n_iter=2000], time_cost:294.7656秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=10 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=10 n_iter=3000], time_cost:433.3698秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=500 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=500 n_iter=1000], time_cost:169.0882秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=500 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=500 n_iter=2000], time_cost:302.5136秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=12 learning_rate=500 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=12 learning_rate=500 n_iter=3000], time_cost:429.2366秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=200 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=200 n_iter=1000], time_cost:188.0710秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=200 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=200 n_iter=2000], time_cost:330.8482秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=200 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=200 n_iter=3000], time_cost:450.2752秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=10 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=10 n_iter=1000], time_cost:161.2984秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=10 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=10 n_iter=2000], time_cost:302.7048秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=10 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=10 n_iter=3000], time_cost:431.5645秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=500 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=500 n_iter=1000], time_cost:191.3594秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=500 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=500 n_iter=2000], time_cost:323.9209秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=100 learning_rate=500 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=100 learning_rate=500 n_iter=3000], time_cost:460.4915秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=200 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=200 n_iter=1000], time_cost:188.7316秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=200 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=200 n_iter=2000], time_cost:313.4260秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=200 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=200 n_iter=3000], time_cost:451.3919秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=10 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=10 n_iter=1000], time_cost:49.9845秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=10 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=10 n_iter=2000], time_cost:51.6042秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=10 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=10 n_iter=3000], time_cost:50.2437秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=500 n_iter=1000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=500 n_iter=1000], time_cost:193.7552秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=500 n_iter=2000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=500 n_iter=2000], time_cost:338.5227秒\n",
            "\n",
            "starting searching [perplexity=100 early_exaggeration=500 learning_rate=500 n_iter=3000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "完成一个！！！\n",
            "ending searching [perplexity=100 early_exaggeration=500 learning_rate=500 n_iter=3000], time_cost:478.6822秒\n",
            "\n",
            "starting searching [perplexity=500 early_exaggeration=12 learning_rate=200 n_iter=1000]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}